The task of predicting a variable-sized output requires a recurrent model such as a long short-term memory (LSTM) network [1]}. We are interested in a generative model that can generate a variable number of SMPL shapes while learning the joint probability of all individuals in a scene based on text. To achieve this, we rely on a recurrent adversarial learning scheme in which we design a recurrent iterative GAN that consists of two modules, namely the generator and the critic. In this learning paradigm, the generator and critic are two neural networks trained simultaneously with an inverse objective, in which the critic aims to differentiate between real and generated samples while the generator aims to fool the critic.
